Original · Unique Research · 2026-07-15
Editor's note: The first-person interview and its judgments belong to the original Chinese author and to Zhai Xingji (翟星吉), founder and CEO of Yuhé Technology (语核科技). This English rendition retains the complete narrative, product description, overseas analysis, decision framework, and all nineteen Q&A items. Financial figures, customer counts, repeat-purchase rates and product claims are founder self-reports, not independently audited findings. Company and product names are preserved as named attributions.
AI Industry Observation
This Agent Company Chose Profitability While Everyone Else Was Burning Cash
"Profit dictates the pace of expansion; product depth dictates the boundary of expansion."
"The Pipeline has an internal ledger. Ten million yuan isn't a slogan shouted off the cuff — it's a range backed out from the funnel."
When he said this, Zhai Xingji himself was a little surprised.
The surprise wasn't that he'd hit the number. He'd done the math long ago. What surprised him was that the deviation was almost zero. He set a 10 million RMB revenue target mid-year, and by year-end, it came out to exactly 10 million.
In the ToB software industry, hitting target and result this closely aligned isn't luck — it's backed by an operating logic that runs almost exactly opposite to most people's.
The more striking numbers come after.
Customer repeat-purchase rate soared from 30% at the start of the year to over 90%. Not from a single quarter's spike — from sustained, stable performance.
30% to 90% — that's not optimization. That's a qualitative change in the business model.
Customer count nearly tripled, from around ten leading enterprises to over thirty. The hardest line: customer churn is zero. Not a single one left.
If you know the 2025 Agent sector ecosystem, you know how abnormal these numbers are.
While the industry's dominant narrative is still stuck in "burn cash for scale" and "get big first, profit later," Yuhé Technology reached monthly break-even back in October 2024. After raising tens of millions in Pre-A funding, it didn't go on a hiring spree, didn't splurge on exposure — instead, it tightened its profit discipline even further.
Put simply, this is a company that, while everyone else was flooring the accelerator, chose to build the brake system first.
Zhai Xingji is founder & CEO of Yuhé Technology. He started the company in May 2023, moving from an "algorithm business" to a "software business," and today has upgraded the product to LangHub, an enterprise-grade Agent training ground anyone can use. His core playbook can be summed up in one sentence: profit dictates the pace of expansion, product depth dictates the boundary of expansion. It sounds like a conservative credo of a traditional entrepreneur — but placed on a startup standing on the hottest track in AI, it sounds almost jarring.
The Agent sector never lacks stories. What it lacks is evidence that the company survives after the story ends.
The First Thing After the Money Lands Isn't Hiring — It's Holding the Line
The first topic we discussed was money. Not how glamorous fundraising is, but how to spend what's raised.
In October 2024, Yuhé Technology reached monthly break-even. Note the timing — this was before the Pre-A round hit the account. In other words, the company first proved internally that "revenue from operations > money spent" worked, and only then took on outside capital. It didn't wait until it was burning empty to beg for rescue; it went to reinforce the walls after it had developed its own blood-making capacity.
"If the funding burned through, could you survive?" I asked Zhai Xingji.
"Yes." The answer was crisp. "Revenue far exceeds expenditure, and the raise itself was far more than we actually need. But we won't spend recklessly because of that."
He ran the numbers. The tens of millions from the Pre-A went mainly to R&D, with a small slice to marketing. The team is now over 60 people; there was no large-scale sales hiring, no funding poured into brand exposure. "Hire as many people as there is profit" — this sentence came up more than once.
I pressed: shouldn't a company accelerate after raising? Hire people, expand into markets, grab the window — that's "what a startup is supposed to look like."
Zhai Xingji's answer was direct: "Funding landing doesn't mean discipline can loosen — it means we have to hold it even tighter. Otherwise the more you raise, the weaker your ability to survive a downturn."
"The usual logic is: funding = the passport to expansion. Get money, prove the model works, then scale. But Zhai Xingji's logic is reversed — funding isn't a reason to expand, it's ammunition to reinforce the moat. What actually decides whether you dare to expand is whether the product can be made deep and stable."
R&D occupies a clearly higher share of his resource allocation. This doesn't need a label of "we take technology seriously" — the actual investment structure says it itself. Why? Because the ToB Agent business doesn't sell a one-off feature; it sells the "productivity infrastructure" that enterprises rely on day after day in real business scenarios. A product sold today must still run stably six months or a year later, keep evolving, or customers won't renew or expand.
Put simply, ToB software repeat purchase isn't decided by customer relationships — it's decided by product depth.
Zhai Xingji has his own understanding of "profit discipline." It's about "investing at the pace of profit" — the precondition for expansion is that the product has solid footing. This approach looks slow in a venture context that worships speed. But from another angle — when the hype fades and capital tightens, companies with profit discipline are the ones still eligible to sit at the table.
"We don't chase short-term trends," he said. "We look at whether customers are still using it three or five years out, whether they can live without it."
I noticed one detail: Yuhé Technology didn't take the "free first, convert later" route. From early project-based algorithm work to standardized software product, Zhai Xingji realized early that "customers cultivated by free habits often have weak willingness to pay."
"Real ToB paying customers don't care about price level — they care whether you can solve the problem closest to their core assets. Once this customer segment is clearly identified, sales efficiency goes up instead of down; you don't need to blanket-cover the market, you precisely serve those willing to pay for results."
Behind this restraint is a clear-eyed understanding of the essence of the business model.
The Agent sector is awash in hot money and concepts. Many people equate "building an Agent" with "wrapping a large-model API to make a demo." Zhai Xingji isn't buying it: "The underlying foundation models are increasingly like water and electricity. The real moat is the production line that keeps running stably in real business scenarios." Anyone can tap into utilities, but turning utilities into a production line that continuously outputs value is the hard part.
The essence of profit discipline is not being thrown off rhythm by noise. Have money but don't spend wildly; see a trend but don't chase blindly. First polish that "production line that runs stably in real business" until it's solid, then talk about expansion. This logic, in today's Agent sector, is itself the rarest awareness.
Not an Upgrade, a Rebuild: From Digital Employees to an Enterprise Knowledge Operating System
After money, we talked product. This is the part that excites Zhai Xingji most.
The 2026 product strategy has completed a generational leap. Yuhé Technology's flagship product is no longer a single-form "digital employee" called Langtum, but the newly launched LangHub — an enterprise-grade Agent training ground anyone can use.
Behind this four-character change is a fundamental rebuild of thinking.
Langtum's logic was "help the enterprise build a digital employee" — targeting a specific role and scenario, training an AI that can replace or assist a human in a specific task. The logic itself is sound. But the real gap isn't that the digital employee "can't remember" — it's that it has no self-evolution capability and can't automatically consolidate the team's experience and methods into the organization's own capability assets. No matter how long you use it, it just repeats execution; it doesn't get to know the enterprise better. Hence LangHub: letting the digital employee continuously self-iterate during use, truly consolidating experience into the enterprise's own knowledge assets.
LangHub's logic is completely different. It's "help the enterprise build an AI foundation that continuously consolidates knowledge assets and amplifies team value."
A metaphor. A traditional Agent gives you a good hoe and sends you to farm. LangHub helps you consolidate generations of farming experience, soil data, and seasonal rhythms into a self-growing farming knowledge base. The longer you use it, the better it understands your land.
In the end, the value of the land itself grows — not just whether your hoe is slightly better than the neighbor's.
Zhai Xingji's own words are more direct: "Is the time paying for software, or is it paying for enterprise appreciation? That's the core difference."
To achieve this leap, LangHub made breakthroughs in four technical directions at the foundational layer.
The first breakthrough is a "three-tier continuous memory architecture." LangHub gives each enterprise Agent three memory layers: global profile memory holds the enterprise's and role's baseline settings; project-context memory holds current work in progress and related information; topic snapshots record the real-time state of each specific conversation.
These three layers can be intelligently invoked under a "four-level on-demand loading" mechanism — no need to load all information every conversation. Instead, like a veteran who has worked at this enterprise for years, it says what it should remember on the tip of its tongue and doesn't let what it shouldn't interfere with the present.
You might think "memory" is a small feature. But in actual Agent usage, it's the critical threshold for whether an AI "is usable." Think about it — you converse with an AI colleague who supposedly served you for half a year, and every time it asks your company's basics and re-confirms the project you're working on. How does that feel? Memory isn't a bonus; it's the hard threshold for a ToB Agent to move from "toy" to "tool."
The second breakthrough is the Agent self-evolution engine. LangHub's Agent can automatically extract skills from daily business interactions, identify and calibrate users' implicit preferences, and intelligently compress and archive long-term memory. Simply put, the features set at factory are just the starting point; the Agent keeps learning and optimizing while serving the enterprise.
"The ability to 'extract skills from the business itself' means what? Put simply, every minute the enterprise invests in LangHub converts into the enterprise's own unique capability. A competitor can copy your software, but not the tacit knowledge and business preferences you've accumulated over two years working alongside AI."
The third and fourth breakthroughs can be discussed together, because they solve the same core problem: making Agents "reliable" in enterprise scenarios.
One side is hallucination control. A large model "confidently talking nonsense" is no longer news, but in enterprise scenarios that's not a joke — it's an accident. LangHub built three layers of defense: source evaluation, scoring the credibility of information the AI cites; intent alignment, ensuring the AI's understanding of user need matches actual business intent; and deviation alerts, proactively warning when an AI answer may veer off factual track. These three layers are embedded in the Agent workflow, forming progressive protection.
The other side is the confidence system and long-horizon task decomposition. Many important enterprise tasks can't be completed in one conversation; following a client project may take weeks across multiple departments. LangHub can decompose such complex tasks into trackable subtask chains and judge each step's reliability in real time through confidence scoring. Once a step deviates too far, the system proactively intervenes to correct it — it doesn't wait until the final result comes out to discover it "went off track."
Four technical directions covered, but I want to stress one point: these technologies don't exist to show off. They all point to one goal — letting Agents run stably in real enterprise business scenarios over the long term.
Zhai Xingji said one sentence that I take as the key to understanding Yuhé Technology's entire product philosophy:
"Anyone can plug into a model API, but turning model capability into something the enterprise uses every day and gets better at using — that is far from solved."
Everyone can tap into utilities, and model APIs are increasingly commoditized. Today you use GPT-4, tomorrow I can use Claude, the day after another domestic new model appears. Model capability is converging fast, but "turning model capability into a productivity foundation the enterprise uses daily and gets better at" is far from solved.
That is precisely the niche LangHub is trying to occupy.
Other Agent companies build "a handy tool" — value delivered once, after one use. LangHub builds "automatically consolidating your team's working experience into self-growing knowledge assets." The longer you use it, the thicker the enterprise-specific knowledge in the system, the higher the enterprise's competitive barrier.
This isn't a version upgrade. It's a fundamental shift from "selling tools" to "building a foundation." From Langtum to LangHub, the product form changed, the value-delivery logic changed, and even the potential ceiling of the business model changed.
Tools can be replaced. Assets only appreciate.
Zhai Xingji sees this path clearly: as more enterprise daily operations are built on LangHub, as the enterprise's most critical business processes and experiential knowledge consolidate on this platform, LangHub is no longer a replaceable software vendor — it becomes part of the enterprise's operating system.
At that point, a competitor trying to pry away this customer would face an unimaginably high cost.
This isn't a distant story. Zero churn among 30+ leading enterprises, 90%+ repeat purchase — this logic is already being validated. Customers paying for "a better AI tool" is one-time; continuously budgeting for "a knowledge infrastructure that helps the enterprise get smarter" is a long-term relationship.
Product decides the boundary. Zhai Xingji didn't say this sentence, but every decision of his confirms it. Profit discipline holds the rhythm of expansion; product depth decides the boundary. When everyone in the Agent sector is racing to run faster, Yuhé Technology chooses to pave the road more solidly first.
Money can be burned into speed, but it can't be burned into retention. This is a plain truth that most people have forgotten.
The Hardest Part of Going Global Isn't the Product — It's Making Customers Believe You
The product works domestically, and the going-global string has always been on Zhai Xingji's calendar. 2025 wasn't a year of "holding the fort" for him; what's validated domestically must be re-validated on a larger scale. In March 2026, LangHub scheduled Japan, Korea, and Southeast Asia into its plan, with a target that overseas revenue reach 10%-20%.
But at the execution layer, the pace is much slower than domestic — Zhai Xingji says this is entirely within expectations. The hardest part of ToB going global has never been the product; it's making customers believe you. An AI Agent platform, however strong its features, however flashy its demo — why would a customer far away hand you their business?
His approach is pragmatic: don't cast a wide net. Start by targeting enterprises with supply-chain ties to China and teams with China backgrounds. These people have already dealt with you; trust cost isn't that high. It's like using an existing relationship network as a springboard. The first order often doesn't require explaining "who are you" from scratch — it goes straight to "what can you solve for me."
Southeast Asia is moving faster than Japan and Korea. Local enterprises' acceptance of new technology is genuinely higher, decision chains are shorter — one meeting can decide. But the trade-off is lower average deal size; to scale you need volume. Zhai Xingji's judgment: Southeast Asia is good for spreading out first and validating, running the product in a real environment — but don't expect one deal to feed you for half a year.
Japan and Korea are the opposite. Decision chains are headache-inducing in length; building local trust takes longer. You might fly three trips and still be talking to middle management, never meeting the person who can sign off. But once you're in, customer willingness to pay and stickiness are higher — "slow to warm up, but worth it." His experience in Japan: the first customer took nearly half a year to close, but after signing, expansion was faster than domestic customers. That culture of rarely switching vendors once it trusts you is completely different from China.
Here, Zhai Xingji offered a counterintuitive insight.
Many people assume Southeast Asia has "lots of Chinese people, close culture" and should be easy. But sitting down to talk, they find concerns about "should data leave the country, should the system be locally deployed" are no fewer than in the US or Europe. Because Southeast Asia's data regulation rules are still being refined, enterprises are more cautious, fearing that what's compliant today won't be tomorrow. Zhai Xingji talked to an Indonesian customer whose CEO was ethnically Chinese, with Mandarin more standard than his own — but when it came to deployment, that CEO's demands on data sovereignty were stricter than Japanese customers'.
"What really 'looks beautiful but is actually hard' isn't a particular country — it's the market where 'culture and language seem close, so it should be easy.' Such markets make you lower your guard; you rush in unprepared and fall the hardest. You think localization isn't needed, but what's required isn't just language localization — it's localization of how trust is built."
Conversely, markets with "big cultural differences," like Japan and Korea, can move faster than expected once you find the right local partner. Because you know it's hard, you do your homework. Local partners help you navigate cultural reefs, introduce key decision-makers, translate the subtext left unsaid — you focus on product and delivery, and you can actually move.
In the end, whether going global is hard has little to do with "cultural distance." Zhai Xingji summarized three factors that truly determine difficulty: clarity of the data-compliance environment, length of the customer decision chain, and whether there's a reliable local partner. Passing the product bar only gets you the entry ticket; these three gates are the real test. Many people think building a good product means done — actually the hardest part has just begun.
Going global requires patience — slowly grind the right things, but immediately fix the wrong ones. This rhythm of "fast when fast is needed, slow when slow is needed" actually has a more down-to-earth phrase inside Yuhé: "slide-kneel."
Insist on yourself one second, then the next second admit you're wrong — that's "slide-kneel"
In September 2025, Zhai Xingji threw out this word at the open mic of Unique Bloom. His exact words: "Strategic firmness doesn't mean grinding to death; being able to slide-kneel is how you go far."
"Slide-kneel" is a meme from esports, originally with a hint of self-deprecation — getting blown up in-game and kneeling to concede. But Zhai Xingji redefined it; it has nothing to do with self-abasement. It expresses approval of rapid iteration. Put simply, it's an ability: one second you're holding to your own judgment, the next you find the other side is right, and you immediately swallow your pride and follow. It has nothing to do with indecision — it's purely that cognitive refresh speed runs faster than ego.
There's no single "iconic slide-kneel moment" to tell as a story. Because this kind of thing happens almost every week at Yuhé. A technical line insisted on last week gets overturned by customer feedback this week; a pricing strategy set last month proves unworkable this month; a direction deemed worth heavy investment at year-start finds mid-year that the market hasn't taken off. There are internal arguments, and after the yelling, you admit what's right.
"Slide-kneel isn't a once-a-year event — it's aunderlying habit in daily decisions. When the whole organization defaults to 'who's right, listen to whom,' iteration speed naturally rises. The worst case is a founder who values face over truth; when data has already slapped him in the face, he still braces. What he's bracing isn't strategic resolve — it's ego."
This culture extends to fundraising strategy. Yuhé prefers "quiet money, no meddling" financial investors — long-term thinkers who share cognitive frequency and can accept the "fast try, fast admit" rhythm. "What we want is aligned long-term thinkers — cognitive alignment matters more than 'industry expertise' or 'resources,'" Zhai Xingji explained.
In the end, "slide-kneel" is reverence for truth. In an AI sector where model capability gets refreshed every three months, recognizing truth matters far more than holding a position. This "reverence for truth" habit cost Zhai Xingji an expensive lesson last year. An industrial customer project — nearly half a year of custom investment went down the drain — but it bought a core judgment that guides all future strategic decisions.
When the model gets smarter, your custom investment instantly goes to zero — that's the fiercest competition
Rewind to early 2024. Zhai Xingji's judgment was "the Agent closest to money is the only way out for AI ToB." This still broadly holds today, but with one critical amendment — "close to money" isn't enough; it has to be "closest to core assets."
The lesson came from an industrial customer. Their production line needed visual recognition fine-tuning for yield-rate detection — sounds core enough, right? It touches production quality and is directly tied to revenue. The Yuhé team threw themselves into deep customization — tuning models, building pipelines, doing integration, fussing for nearly half a year. The customer was happy, results were good, Phase 1 delivered smoothly.
Half a year later, a general VL large model upgraded and directly achieved the same tier of visual recognition. That early pile of custom investment — labor, time, emotion — instantly went to zero.
"That's when I realized, 'close to money' doesn't equal 'close to the customer's core assets.' Models get smarter; what only you can do today, open-source models may do in half a year. What truly doesn't change? The business logic, decision processes, and industry know-how an enterprise has built over decades. These don't depreciate with model upgrades — they appreciate the more they consolidate."
So the judgment must upgrade. Real "close to money" means not only solving today's blockage, but also helping the customer consolidate the proprietary logic of "how to handle these things." No matter how smart the model gets, it doesn't know how this factory sets production priorities, how that trading company tiers customers, or what unspoken rules guide case allocation at this law firm. These are the moats Yuhé wants to dig into.
But identifying "truly high-value" scenarios is easier said than done. Over the past year Zhai Xingji stepped into two types of pitfalls, each costing tuition.
The first type is "looks interesting, but willingness to pay doesn't materialize." AI email summaries, auto-generated daily reports, smart meeting minutes — during demos customers' eyes light up, "this is great this is great," and when you ask about budget: "we'll study this internally." Because it doesn't map to quantifiable operating gains, procurement can't move. Put simply, saving time is nice for workers, but it doesn't constitute a procurement push for the boss.
The second type is more hidden: the scenario itself works, but it's plugged into the wrong spot. For example, raising quality-inspection accuracy from 92% to 97% — the number looks great, the customer nods on the spot. But if the line's real bottleneck is production scheduling — orders pile up and can't be sequenced, machines idle waiting for materials — no matter how good inspection is, it just waits; overall operating metrics don't move, and willingness to pay drops accordingly. Three months later, renewal is a disaster.
"Often we, together with the customer, confuse 'looks high-value' with 'truly high-value stuck at the core node of the decision chain,'" Zhai Xingji said. This discriminative ability is itself an Agent company's core competence. Not doing icing-on-the-cake things, but doing charcoal-in-the-snow things — and delivering them to the right spot, not to the neighbor's house.
Speaking of competition, big tech can't be avoided. Zhai Xingji has thought this through.
In the short term he judges big tech "structurally can't do application-layer innovation well" — department walls, layer upon layer of approval, KPIs that can't tolerate deep scenario work with no short-term output. Big tech sells platforms and tools; Yuhé sells deployment solutions and services already running in customers' businesses. "It's not a capability problem, it's a structural problem. A big-tech product manager who wants to go deep into a vertical scenario has to go through how many layers of approval? In three months, a startup has already run a validation cycle."
"What really decides the risk isn't how much money big tech has on its books," Zhai Xingji paused, "but whether they can settle down and produce deeper scenario insight. I judge they structurally cannot."
His confidence also comes from a moreunderlying judgment: most people globally haven't yet deeply interacted with AI; market penetration is extremely low; talking about "head-on competition" is premature. We're not yet at the stage of grabbing cake — we're still baking it. The blue ocean is big enough; first chew deep on the slice in front of you. Instead of worrying about big tech, worry about whether your own scenario insight is deep enough and your iteration fast enough.
At this point, the conversation naturally turned to advice. Zhai Xingji paused and said he actually dislikes giving "standardized checklists" — every sector, team, and moment is different, and prescribing can mislead. But there is one judgment framework he's growing more certain about, and he's figured out how to express it.
Is the time paying for software, or for enterprise appreciation?
The framework's core is: whether the scenario has a "boundary where tacit knowledge keeps being produced."
Traditional software and RPA solve "visible fixed processes" — expense approval, data entry, form routing. The logic is clear, rules are hard-coded, boundaries are explicit.
What Agents must do is rebuild "invisible decision-making gray zones" — the experience a master passes on after twenty years of apprenticeship, the instinct a sales director uses to judge a customer's intent from the first meeting, the subtle calibration a procurement manager has with suppliers. These have no SOP, but they decide an enterprise's competitiveness.
LangHub's positioning sits right here: letting the expert who knows the business best, without touching code, consolidate decision logic into reusable role Skills. Not replacing human decision-making with AI, but structuring the expert's judgment method into the organization's own capability asset — the person in the team who knows the business best has their experience continuously consolidated on the platform, reused by more people, instead of living only in their head.
The path sounds plain: "from the scenario, back to the platform." Chew one scenario at a time, master the business logic, abstract it into reusable modules on the platform. Slow? Yes. Each bite requires spending time soaking with the customer — understanding their business, their KPIs, the thing that keeps them up at night. But once it works, it becomes a foundation the enterprise can't live without.
"Every Agent entrepreneur should ask themselves one question: when the customer pays this money, are they paying for software, or for enterprise appreciation? If the answer is the former, sooner or later you'll be flattened by a model upgrade."
The sky outside the window had already darkened. Before the interview ended, Zhai Xingji added one more line: what's most fascinating about this sector is precisely that no one knows the standard answer. The unpredictability itself is the threshold — those who dare enter, are willing to iterate, and can admit being wrong have the chance to go to the end.
The rest is up to time.
Selected Interview Q&A
Q1 | Mid-2025 you projected full-year revenue of 10 million RMB. Did it land?
Zhai Xingji: It landed, and quite precisely. Mid-year we projected 10 million; year-end we counted, it was exactly 10 million, almost no deviation.
Honestly, I was a little surprised myself by this precision. But it wasn't a pre-calculated coincidence — it's that ToB revenue rhythm is actually predictable. Customer signing cycles, conversion rates, Pipeline in flight — we had the numbers internally mid-year.
So 10 million wasn't a slogan shouted off the cuff; it was a relatively certain range backed out from the funnel at the time. The year-end number landing mostly validated that this prediction method itself holds up, not luck.
Q2 | Where are Yuhé Technology's customer repeat-purchase rate and customer count now?
Zhai Xingji: Repeat purchase among benchmark customers has reached over 90%. This number wasn't driven by promotions or sales chasing orders — it's the product genuinely running at the customer every day, genuinely producing value.
Customers do their own math; once the math is clear, renewal is natural. We don't need to persuade anyone.
On customer count, I earlier said "10+ leading customers"; now it's grown to 30+ leading enterprise customers, nearly tripling. The logic behind this is the same as the revenue forecast — it's not luck, it's that the product works well, so renewals and referrals naturally follow.
Q3 | After completing the tens-of-millions Pre-A round, where did the money mainly go?
Zhai Xingji: Most went to R&D, into polishing the core product, with a small part to marketing — fully consistent with the pre-funding plan.
We've always believed the ToB moat is product power, not noise. What enterprise scenarios truly need solved is: can the system run every day, does it remember, does it hallucinate? These require continuousunderlying technical optimization.
On marketing, our approach is restrained; we didn't burn money for exposure, but made precise investments aligned with business rhythm. The sales team didn't scale up either; we still hold to "hire as many as profit supports," keeping cash flow healthy. The team is about 60+ people now.
Q4 | After funding, did the company's requirements around profitability and expansion change?
Zhai Xingji: No, and it's executed more strictly.
We reached break-even in October 2024 and have stayed healthy since. Funding landing doesn't mean the "profit dictates expansion rhythm" discipline can loosen — it has to be held tighter.
If it were truly "raise money, trade it for scale," the first move would be hiring sales and grabbing territory. We didn't do that. Money went mainly to R&D, because what really decides whether we dare expand is whether the product and scenario can be made deep and stable.
Having more money on the account doesn't mean the company can drop operating discipline. Otherwise the more you raise, the weaker the company's risk resistance — the opposite of our original intent.
Q5 | If the funding environment suddenly worsens, can Yuhé Technology's current model survive independently?
Zhai Xingji: Yes.
We're already in a state of self-generated blood and self-profitability; whether there's funding doesn't affect normal operations and rapid development. Revenue far exceeds expenditure, and this raise itself exceeded what the company actually needed, so we're not short of money now.
Companies still burning cash and relying on continuous funding to operate would have a headache if funding goes badly — but that's not our situation.
Q6 | From the Langtum digital employee to LangHub, what changed in Yuhé Technology's product strategy?
Zhai Xingji: In 2026, our product strategy completed an important generational upgrade.
The flagship product is no longer a single digital employee but LangHub — the enterprise-grade Agent training ground anyone can use. Langtum was our technical foundation; LangHub abstracts the capabilities built from digital employees into a knowledge operating system the enterprise can autonomously harness.
It can automatically consolidate the enterprise's business processes and methodologies into reusable role Skills, embedded directly into business processes to deliver, while having self-evolution capability.
In other words, we moved from "helping the enterprise build a digital employee" to "helping the enterprise build an AI infrastructure that continuously consolidates knowledge assets and amplifies team value."
Q7 | What is LangHub's core technical innovation?
Zhai Xingji: In one sentence, LangHub can both complete complex deep tasks and consolidate an individual's tacit methodology into enterprise-reusable digital assets, while guaranteeing enterprise-grade security and control.
Specifically, we attacked four key points.
First is the three-tier continuous memory architecture. We split memory into global profile, project context, and topic snapshot layers, combined with a four-level on-demand loading mechanism, so the system is like a veteran who's worked at the enterprise for years — remembers long-term accumulated things, rather than starting from zero each conversation.
Second is the Agent self-evolution engine, including automatic Skill extraction, implicit preference calibration, and automatic memory compression. The system can distill reusable skills from the business process and continuously calibrate its understanding of the enterprise and team.
Third is the three-layer anti-hallucination architecture: source evaluation, intent alignment, and deviation alert — solving what enterprises worry about most, "confidently fabricating."
Fourth is the confidence system and long-horizon task decomposition. Through confidence scoring, task planning, Workflow Agents, and goal anchors, the system can break complex long-cycle tasks into step-by-step executable and verifiable actions.
Q8 | What is the essential difference between LangHub and other Agent products?
Zhai Xingji: Other Agent products are mostly "give you a handy tool," and it's done once used.
LangHub is different. It automatically consolidates the team's working experience and methods into self-growing knowledge assets. The longer you use it, the more valuable the enterprise itself becomes.
So the core difference is: is the time paying for software, or for enterprise appreciation?
Q9 | Why won't competing purely on foundational model capability become Yuhé Technology's moat?
Zhai Xingji: Underlying foundation models are increasingly like water and electricity — shared infrastructure for everyone. Purely competing on model capability can't be our long-term moat.
The real moat is whether, in the enterprise's real business scenarios, you can build a production line that remembers, can be trusted, and survives long tasks.
We don't chase how flashy one demo is; we look at whether it can run stably in real, continuous business processes. That's also why LangHub must enter leading customers' real business processes rather than stay at the demo level.
Q10 | How is Yuhé Technology's overseas progress? Why is ToB going global slower than expected?
Zhai Xingji: The overseas pace is indeed slower than domestic, but it's within our expectations.
The hardest part of ToB going global has never been whether the product works, but how to build trust. Overseas customers facing a Chinese AI company with no local history naturally add a layer of caution, and decision chains are longer than domestically.
Our current play is to start with enterprises tightly linked to Chinese supply chains and Chinese teams, use these easier-to-establish-trust customers for the first batch of validation, then expand outward.
As for whether there's strictly a first overseas paying customer, I'd rather wait until it's truly running stably, with repeat purchase and usage data, before talking publicly — rather than forcing a case to hit a timeline.
Q11 | What are the characteristics of the Southeast Asian vs. Japan/Korea markets?
Zhai Xingji: Right now Southeast Asia is moving a bit faster.
Local enterprises are more open to trying new technology, decision chains are relatively shorter, and it's easy to start small-scale first; but average deal sizes are generally low, requiring scale to thicken the business.
Japan and Korea move slower, mainly because decision chains are longer and building local trust takes more time; early on it can look like "big thunder, little rain." But once trust is built, customers' willingness to pay and cooperation stickiness are usually higher — "slow to warm up, but worth it."
My takeaway now is: whether going global is hard has little to do with so-called cultural distance; it depends more on the local data-compliance environment, decision-chain length, and whether you can find a local partner willing to vouch for you.
Q12 | Is a 60+ person team still "small and beautiful"?
Zhai Xingji: The team is about 60+ people, average birth year around 2001, with graduate and undergraduate backgrounds roughly half and half.
We're still deliberately controlling the people-to-output ratio, aiming for a high-productivity organization. Since we ourselves help enterprises use AI for efficiency, we must ourselves be the best practitioner of this method.
So the org structure will stay lightweight; the people-to-output ratio must reach industry-top level. "Small and beautiful" isn't simply having few people — it's that the organization can't become inefficient as it scales.
Q13 | What exactly does your "slide-kneel" theory mean?
Zhai Xingji: "Slide-kneel" isn't self-abasement; it's a cultural expression of rapid iteration.
It's an important trait we use to judge a person or team's state: one second you're holding to your judgment, the next second you find the other side is right, and you can immediately swallow your pride and follow. This ability is rarer than grinding to the end.
Big and small adjustments happen almost weekly internally; if the judgment is wrong, change it; if the direction is off, pivot. So "slide-kneel" isn't a once-a-year event worth singling out — it's aunderlying habit in the team's daily decisions.
Q14 | What correction did the "Agent closest to money" judgment undergo after practice?
Zhai Xingji: This judgment wasn't slapped in the face — it got stronger validation.
What I mean by "closest to money" is essentially that a ToB Agent must grow on the customer's revenue or cost node, not be an icing-on-the-cake edge tool.
But one industrial customer case made my definition of "close to money" stricter. We once did production-line visual recognition fine-tuning for a customer, thinking it directly touched yield rate and was core enough. Half a year later, a general vision-language model upgraded and directly achieved the same capability; our early custom investment nearly instantly went to zero.
This made me realize that true "close to money" means not only solving today's blockage, but also helping the enterprise consolidate the proprietary business logic and workflow of "how to handle these problems."
Models get smarter, but the process assets and know-how an enterprise has built over decades are always scarce. So "closest to money" needs one more clause: closest to the enterprise's core assets.
Q15 | Why do many seemingly valuable enterprise AI scenarios still end with customers unwilling to pay?
Zhai Xingji: Internally we always say tool value equals leverage times base. But "B-end equals high value" still needs further breakdown.
When enterprises pick scenarios, they habitually choose "looks interesting" ones — email summarization, auto-generated daily reports. The demo looks great, customers think it's cool, but on careful calculation these scenarios often don't map to quantifiable gains, so willingness to pay naturally doesn't rise.
Some scenarios do produce results — e.g., AI raising quality-inspection accuracy. But if the line's real bottleneck is scheduling rather than inspection, then even if inspection accuracy rises, the operating metric the factory cares about most doesn't move.
Customers do the math. Once they find "this improvement didn't move the number I care about most," willingness to pay drops.
So the more accurate framing isn't "high-value scenarios don't pay up," but that often we, together with the customer, confuse "looks high-value" with "truly high-value stuck on the core decision chain." The two look similar on the surface, but whether the customer pays differs wildly.
Q16 | Which is a bigger threat to Yuhé Technology — generic Agent platforms, or AI-ification of traditional SaaS companies?
Zhai Xingji: Closer to generic Agent platforms, not the AI-ification upgrade of traditional SaaS companies.
Traditional SaaS plus AI features is still essentially selling software seats; it hasn't truly entered the layer of "whether it can keep running in the business."
Generic Agent platforms wrap model and platform capabilities into tools customers can self-build, making technically strong customers feel "I can build one myself" — this does siphon off some first-wave interest.
But what customers really want is a solution that runs in the business immediately, without raising their own ops team. Big tech sells platforms and tools; we sell deployment solutions and services already running and iterating in customer business — two different businesses.
Short-term risk is manageable, but can't be complacent. What really decides risk isn't how much money big tech has or how strong its models are, but whether it can settle down and produce deep enough scenario insight.
Q17 | After big tech enters the Agent application layer, how should startups build a moat?
Zhai Xingji: We won't wait for the aircraft carrier to arrive before hiding; we thicken our moat ahead of time.
Technically we keep up with the frontier; on product we accumulate through deep scenario insight the business data and know-how others can't copy; on commercialization we rely on real paid validation of value, not burning cash for users.
These three layers reinforce each other. Data from real customer payment feeds back into technical and product iteration.
Right now overall market penetration is still very low; most people globally haven't even deeply interacted with AI. The sector is still blue ocean. The real test may come only after the market matures. By then, what's contested is depth of scenario insight, user understanding, and frontier technical innovation — all three are indispensable.
Q18 | What kind of AI Agent scenario is truly worth a B2B startup investing in?
Zhai Xingji: We judge whether a scenario is worth doing not by whether the tech is cool, but by four steps.
First clearly define which role and which specific problem to solve; then lock the ICP, the ideal customer profile; next find your unique advantage in this scenario; finally think about how to embed these capabilities into LangHub's platform capability.
For example, in complex decision scenarios, what hurts customers most is often not lacking a tool, but that experience can't be passed down. We don't pursue a universal Agent; we consolidate experts' tacit problem-solving logic into reusable role Skills.
This "from the scenario, back to the platform" path is slow, but once it works, it becomes infrastructure the enterprise can't live without.
So my advice: don't rush to chase trends; go back to your ICP, grow your advantage into the product, and let customers pay because they can't live without the assets you help them consolidate.
Q19 | Is there a standardized checklist for judging whether a scenario suits Agents?
Zhai Xingji: Frankly, in the AI Native era, no one can directly hand you a mechanically executable standard checklist.
What makes this sector fascinating is precisely its unpredictability. Internally we watch a dynamic metric: whether the scenario has an "emergence boundary for tacit assets."
Traditional software and RPA solve visible fixed processes; Agents rebuild invisible decision gray zones.
So don't search the world for a so-called standard answer. Go directly into the enterprise, see where the expert who knows the business best is stuck, use the product to solve their pain point and amplify their personal value, then consolidate this methodology into enterprise-reusable assets.
When you truly unlock the scenario that has the customer by the throat, giving ordinary employees multiple-fold output gains, making money and scaling are just natural results.